Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Research and Community Empowerment International Journal of Advances in Artificial Intelligence and Machine Learning
Arqoma Nurveda Carreza
Universitas Negeri Surabaya
Chemistry Computer Science & IT Education Mathematics Physics Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Empowering the Practice-Based Mentoring in Microteaching on Pre-Service ICT Teachers: High and Low Self-Efficacy Analysis Husni Mubarok; Yossiri Yossatorn; Hirnanda Dimas Pradana; Syaiputra Wahyuda Meisa Diningrat; Arqoma Nurveda Carreza; Favian Avila Syahmi
International Journal of Research and Community Empowerment Vol. 4 No. 1 (2026): February 2026
Publisher : Mitra Edukasi dan Publikasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58706/ijorce.v4n1.p14-21

Abstract

Practice-based microteaching is now an important method in pre-service teacher education, particularly in ICT, as it creates a forum to practice teaching skills in a structured setting. However, the success of this depends greatly on the quality of guidance, which has the capacity to deliver timely and critical feedback. Self-efficacy, being the belief that an individual is capable of completing tasks, has been a significant predictor of learning achievement and motivation. The present research aims to examine the pre-service ICT teachers' learning achievement and the learning motivation of high and low self-efficacy individuals within a practice-based microteaching environment supported by mentoring. In experimental design, the subjects (N = 82) were divided into high and low self-efficacy according to a standardized self-efficacy scale. The data analysis in this study employed the t-test and Analysis of Covariance (ANCOVA). The findings of this study indicated there was no difference in learning achievement between the two groups on practice-based mentoring in microteaching for pre-service ICT teachers. Moreover, in intrinsic motivation, it was found that high self-efficacy practice-based mentoring microteaching students have significantly higher intrinsic motivation than low self-efficacy students. In the present study, however, low self-efficacy students for practice-based mentoring microteaching show significantly greater extrinsic motivation than high self-efficacy students. This research offers additional reference to scholars, teachers, and policymakers in investigating the role of self-efficacy in learning activity, learners' accomplishment in learning, and to supporting SDGs 4 and 5 in promoting the quality of education as well as gender equality.
AI-Driven Adaptive Online Digital Modules for Communication Courses Using Learning Analytics and Natural Language Processing Utari Dewi; Andi Kristanto; Atan Pramana; Husni Mubarok; Rizki Fitri Rahima Uulaa; Arqoma Nurveda Carreza; Favian Avila Syahmi; Makibane Daniel Ntlhane
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 2 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i2.689

Abstract

Background: The rapid growth of online learning in higher education requires innovative solutions that support independent, scalable, and standardized learning experiences. Artificial Intelligence (AI), particularly Learning Analytics (LA) and Natural Language Processing (NLP), offers opportunities to enhance digital learning through adaptive content delivery, personalized learning pathways, and automated formative feedback.Aims: This study aimed to develop and evaluate an AI-driven adaptive online digital module for Communication courses that supports personalized learning and standardized instruction across higher education institutions using Gemini.Methods: This study employed the ADDIE development model, comprising Analysis, Design, Development, Implementation, and Evaluation. Learning Analytics was used to monitor student engagement and learning progress, while NLP analyzed students' written responses to generate automated formative feedback. The module was validated by instructional design, subject matter, and media experts, followed by individual and small-group trials. Its effectiveness was evaluated using normalized gain (N-Gain) analysis.Results: Expert validation, individual trials, and small-group evaluations indicated that the developed module achieved a "very good" level of feasibility. The effectiveness evaluation produced a high N-Gain score (0.7), indicating a substantial improvement in student learning outcomes. The integration of Learning Analytics and NLP supported adaptive learning, timely feedback, and increased student engagement.Conclusion: The AI-driven adaptive digital module is feasible and effective for supporting online learning in Communication courses. Integrating Learning Analytics and Natural Language Processing enables personalized instruction and data-informed learning support, making the module a promising approach for improving learning quality in higher education.
Co-Authors Andi Kristanto Atan Pramana Favian Avila Syahmi Husni Mubarok Makibane Daniel Ntlhane Rizki Fitri Rahima Uulaa Syaiputra Wahyuda Meisa Diningrat Utari Dewi Yossiri Yossatorn